def train(self, trainingSet):
"""
This function train the ROM this gate is linked to. This method is aimed to agnostically understand if a "time-dependent-like" ROM needs to be constructed.
@ In, trainingSet, dict or list, data used to train the ROM; if a list is provided a temporal ROM is generated.
@ Out, None
"""
if type(trainingSet).__name__ not in 'dict':
self.raiseAnError(IOError, "The training set is not a dictionary!")
if len(trainingSet.keys()) == 0:
self.raiseAnError(IOError, "The training set is empty!")
if any(type(x).__name__ == 'list' for x in trainingSet.values()):
# we need to build a "time-dependent" ROM
self.isADynamicModel = True
if self.pivotParameterId not in trainingSet.keys():
self.raiseAnError(
IOError, "the pivot parameter " + self.pivotParameterId +
" is not present in the training set. A time-dependent-like ROM cannot be created!"
)
if type(trainingSet[self.pivotParameterId]).__name__ != 'list':
self.raiseAnError(
IOError, "the pivot parameter " + self.pivotParameterId +
" is not a list. Are you sure it is part of the output space of the training set?"
)
self.historySteps = trainingSet.get(self.pivotParameterId)[-1]
if len(self.historySteps) == 0:
self.raiseAnError(IOError, "the training set is empty!")
if self.canHandleDynamicData:
# the ROM is able to manage the time dependency on its own
self.supervisedContainer[0].train(trainingSet)
else:
# we need to construct a chain of ROMs
# the check on the number of time steps (consistency) is performed inside the historySnapShoots method
# get the time slices
newTrainingSet = mathUtils.historySnapShoots(
trainingSet, len(self.historySteps))
if type(newTrainingSet).__name__ != 'list':
self.raiseAnError(IOError, newTrainingSet)
# copy the original ROM
originalROM = copy.deepcopy(self.supervisedContainer[0])
# start creating and training the time-dep ROMs
self.supervisedContainer = [
] # [copy.deepcopy(originalROM) for _ in range(len(self.historySteps))]
# train
for ts in range(len(self.historySteps)):
self.supervisedContainer.append(copy.deepcopy(originalROM))
self.supervisedContainer[-1].train(newTrainingSet[ts])
else:
#self._replaceVariablesNamesWithAliasSystem(self.trainingSet, 'inout', False)
self.supervisedContainer[0].train(trainingSet)
self.amITrained = True
def train(self, trainingSet, assembledObjects=None):
"""
This function train the ROM this gate is linked to. This method is aimed to agnostically understand if a "time-dependent-like" ROM needs to be constructed.
@ In, trainingSet, dict or list, data used to train the ROM; if a list is provided a temporal ROM is generated.
@ In, assembledObjects, dict, optional, objects that the ROM Model has assembled via the Assembler
@ Out, None
"""
if type(trainingSet).__name__ not in 'dict':
self.raiseAnError(IOError, "The training set is not a dictionary!")
if not list(trainingSet.keys()):
self.raiseAnError(IOError, "The training set is empty!")
# provide assembled objects to supervised container
if assembledObjects is None:
assembledObjects = {}
self.supervisedContainer[0].setAssembledObjects(assembledObjects)
# if training using ROMCollection, special treatment
if isinstance(self.supervisedContainer[0],
SupervisedLearning.Collection):
self.supervisedContainer[0].train(trainingSet)
else:
# not a collection # TODO move time-dependent snapshots to collection!
## time-dependent or static ROM?
if any(type(x).__name__ == 'list' for x in trainingSet.values()):
# we need to build a "time-dependent" ROM
self.isADynamicModel = True
if self.pivotParameterId not in list(trainingSet.keys()):
self.raiseAnError(
IOError,
'The pivot parameter "{}" is not present in the training set.'
.format(self.pivotParameterId),
'A time-dependent-like ROM cannot be created!')
if type(trainingSet[self.pivotParameterId]).__name__ != 'list':
self.raiseAnError(
IOError,
'The pivot parameter "{}" is not a list.'.format(
self.pivotParameterId),
" Are you sure it is part of the output space of the training set?"
)
self.historySteps = trainingSet.get(self.pivotParameterId)[-1]
if not len(self.historySteps):
self.raiseAnError(IOError, "the training set is empty!")
# intrinsically time-dependent or does the Gate need to handle it?
if self.canHandleDynamicData:
# the ROM is able to manage the time dependency on its own
self.supervisedContainer[0].train(trainingSet)
else:
# TODO we can probably migrate this time-dependent handling to a type of ROMCollection!
# we need to construct a chain of ROMs
# the check on the number of time steps (consistency) is performed inside the historySnapShoots method
# get the time slices
newTrainingSet = mathUtils.historySnapShoots(
trainingSet, len(self.historySteps))
assert type(newTrainingSet).__name__ == 'list'
# copy the original ROM
originalROM = self.supervisedContainer[0]
# start creating and training the time-dep ROMs
self.supervisedContainer = [
] # [copy.deepcopy(originalROM) for _ in range(len(self.historySteps))]
# train
for ts in range(len(self.historySteps)):
self.supervisedContainer.append(
copy.deepcopy(originalROM))
self.supervisedContainer[-1].train(newTrainingSet[ts])
# if a static ROM ...
else:
#self._replaceVariablesNamesWithAliasSystem(self.trainingSet, 'inout', False)
self.supervisedContainer[0].train(trainingSet)
# END if ROMCollection
self.amITrained = True
